Changing dividend payout behavior and predicting dividend policy in emerging markets: Evidence from India


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Dividends have become increasingly important for capital market participants to achieve financial goals in the rapidly changing Indian economy. This study aims to simplify the evolving Indian dividend puzzle by analyzing the dividend trends, examining the evolving nature of firm and macroeconomic determinants of dividends, and developing a dividend policy prediction model. Dividend trends of 3,162 non-financial listed Indian firms from 2006–2022 are studied to gain insights about the Indian dividend puzzle. Regularization and logit models are used to explore the nature of impact of important dividend determinants. Data-mining methods are employed to build a robust model for dividend policy prediction. Trend analysis reveals a decline in the quantum of dividends and proportion of dividend-paying firms with approximately 90% of the dividend-payers belonging to the manufacturing and service sector. Further findings suggest that size, age, maturity, profitability, past dividends, earnings, and bank monitoring of firms had a favorable impact on the likelihood of dividend payments. Macroeconomic indicators such as GDP growth rate, repo rate, percentage change in equity issues, listings, gross fixed assets formation also had a positive impact. The annual percentage change in debt issues and new project announcements at the macro level with investment prospects at firm level negatively impacted dividends. Dividend prediction model based on the random forest technique achieved the highest prediction accuracy of 90.77% and 77.31% under binomial and multi-class situations. These findings are expected to help corporate executives, portfolio managers and investors proactively design optimal dividend policies and formulate their investment strategies.

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    • Table 1. Dividend payer and non-payer firm composition
    • Table 2. Trends in dividend payouts
    • Table 3. Decomposition of payers and non-payers into firms with positive and negative EPS
    • Table 4. Industry composition
    • Table 5. Descriptive statistics
    • Table 6. Variables selected by regularization methods and logit results of the two sparse models
    • Table 7. Binomial scenario dividend policy prediction results
    • Table 8. Multiclass scenario dividend policy prediction results
    • Table A1. Variable description
    • Conceptualization
      Amit Kumar, Pankaj Sinha
    • Data curation
      Amit Kumar
    • Formal Analysis
      Amit Kumar, Pankaj Sinha
    • Investigation
      Amit Kumar, Pankaj Sinha
    • Methodology
      Amit Kumar, Pankaj Sinha
    • Project administration
      Amit Kumar, Pankaj Sinha
    • Software
      Amit Kumar
    • Supervision
      Amit Kumar, Pankaj Sinha
    • Validation
      Amit Kumar, Pankaj Sinha
    • Visualization
      Amit Kumar, Pankaj Sinha
    • Writing – original draft
      Amit Kumar, Pankaj Sinha
    • Writing – review & editing
      Amit Kumar, Pankaj Sinha